AUTOMATED AND ACCURATE ORIENTATION OF COMPLEX IMAGE SEQUENCES

The paper illustrates an automated methodology capable of finding tie points in different categories of images for a successive orientation and camera pose estimation procedure. The algorithmic implementation is encapsulated into a software called ATiPE. The entire procedure combines several algorithms of both Computer Vision (CV) and Photogrammetry in order to obtain accurate results in an automated way. Although there exist numerous efficient solutions for images taken with the traditional aerial block geometry, the complexity and diversity of image network geometry in close-range applications makes the automatic identification of tie points a very complicated task. The reported examples were made available for the 3D-ARCH 2011 conference and include images featuring different characteristics in terms of resolution, network geometry, calibration information and external constraints (ground control points, known distances). In addition, some further examples are shown, that demonstrate the capability of the orientation procedure to cope with a large variety of block configurations

Detector-Free Structure from Motion

We propose a new structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as the first step, which is difficult for texture-poor scenes, and poor keypoint detection may break down the whole SfM system. We propose a new detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints, while solving the multi-view inconsistency issue of detector-free matchers. Specifically, our framework first reconstructs a coarse SfM model from quantized detector-free matches. Then, it refines the model by a novel iterative refinement pipeline, which iterates between an attention-based multi-view matching module to refine feature tracks and a geometry refinement module to improve the reconstruction accuracy. Experiments demonstrate that the proposed framework outperforms existing detector-based SfM systems on common benchmark datasets. We also collect a texture-poor SfM dataset to demonstrate the capability of our framework to reconstruct texture-poor scenes. Based on this framework, we take $\textit{first place}$ in Image Matching Challenge 2023.Comment: Project page: https://zju3dv.github.io/DetectorFreeSfM

3D Reconstruction of Historic Landmarks from Flickr Pictures

Tato práce popisuje problematiku návrhu a vývoje aplikace pro rekonstrukci 3D modelů z 2D obrazových dat, označované jako bundle adjustment. Práce analyzuje proces 3D rekonstrukce a důkladně popisuje jednotlivé kroky. Prvním z kroků je automatizované získání obrazové sady z internetu. Je představena sada skriptů pro hromadné stahování obrázků ze služeb Flickr a Google Images a shrnuty požadavky na tyto obrázky pro co nejlepší 3D rekonstrukci. Práce dále popisuje různé detektory, extraktory a párovací algoritmy klíčových bodů v obraze s cílem najít nejvhodnější kombinaci pro rekonstrukci budov. Poté je vysvětlen proces rekonstrukce 3D struktury, její optimalizace a jak je tato problematika realizovaná v našem programu. Závěr práce testuje výsledky získané z implementovaného programu pro několik různých datových sad a porovnává je s výsledky ostatních podobných programů, představených v úvodu práce.This thesis describes challenges in design and development of an application which reconstructs 3D model given set of 2D images. This technique is called bundle adjustment. The thesi discusses the 3D reconstruction pipeline and elaborates on each step. The first step covers dataset acquisition from the internet. The scripts used to download such data from Flickr and Google Images are described and image characteristics necessary for a good reconstruction are identified. Hereafter the paper compares different feature detectors, extractors and matchers to find best suited combination for reconstruction of historic landmarks. This is followed by description the reconstruction and optimization steps and their implementation. At the end of the thesis the implemented solution is examined on several datasets and compared with other existing solutions presented at the very beginning of the thesis.

To 3D or Not 3D: Choosing a Photogrammetry Workflow for Cultural Heritage Groups

The 3D reconstruction of real-world heritage objects using either a laser scanner or 3D modelling software is typically expensive and requires a high level of expertise. Image-based 3D modelling software, on the other hand, offers a cheaper alternative, which can handle this task with relative ease. There also exists free and open source (FOSS) software, with the potential to deliver quality data for heritage documentation purposes. However, contemporary academic discourse seldom presents survey-based feature lists or a critical inspection of potential production pipelines, nor typically provides direction and guidance for non-experts who are interested in learning, developing and sharing 3D content on a restricted budget. To address the above issues, a set of FOSS were studied based on their offered features, workflow, 3D processing time and accuracy. Two datasets have been used to compare and evaluate the FOSS applications based on the point clouds they produced. The average deviation to ground truth data produced by a commercial software application (Metashape, formerly called PhotoScan) was used and measured with CloudCompare software. 3D reconstructions generated from FOSS produce promising results, with significant accuracy, and are easy to use. We believe this investigation will help non-expert users to understand the photogrammetry and select the most suitable software for producing image-based 3D models at low cost for visualisation and presentation purposes

Modeling and Calibrating the Distributed Camera

Structure-from-Motion (SfM) is a powerful tool for computing 3D reconstructions from images of a scene and has wide applications in computer vision, scene recognition, and augmented and virtual reality. Standard SfM pipelines make strict assumptions about the capturing devices in order to simplify the process for estimating camera geometry and 3D structure. Specifically, most methods require monocular cameras with known focal length calibration. When considering large-scale SfM from internet photo collections, EXIF calibrations cannot be used reliably. Further, the requirement of single camera systems limits the scalability of SfM. This thesis proposes to remove these constraints by instead considering the collection of cameras as a distributed camera that encapsulates the image and geometric information of all cameras simultaneously. First, I provide full generalizations to the relative camera pose and absolute camera pose problems. These generalizations are more expressive and extend the traditional single-camera problems to distributed cameras, forming the basis for a novel hierarchical SfM pipeline that exhibits state-of-the-art performance on large-scale datasets. Second, I describe two efficient methods for estimating camera focal lengths for the distributed camera when calibration is not available. Finally, I show how removing these constraints enables a simpler, more scalable SfM pipeline that is capable of handling uncalibrated cameras at scale